Tracing Evolving Clusters by Subspace and Value Similarity

  • Stephan Günnemann
  • Hardy Kremer
  • Charlotte Laufkötter
  • Thomas Seidl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6635)

Abstract

Cluster tracing algorithms are used to mine temporal evolutions of clusters. Generally, clusters represent groups of objects with similar values. In a temporal context like tracing, similar values correspond to similar behavior in one snapshot in time. Each cluster can be interpreted as a behavior type and cluster tracing corresponds to tracking similar behaviors over time. Existing tracing approaches are designed for datasets satisfying two specific conditions: The clusters appear in all attributes, i.e. fullspace clusters, and the data objects have unique identifiers. These identifiers are used for tracking clusters by measuring the number of objects two clusters have in common, i.e. clusters are traced based on similar object sets.

These conditions, however, are strict: First, in complex data, clusters are often hidden in individual subsets of the dimensions. Second, mapping clusters based on similar objects sets does not reflect the idea of tracing similar behavior types over time, because similar behavior can even be represented by clusters having no objects in common. A tracing method based on similar object values is needed. In this paper, we introduce a novel approach that traces subspace clusters based on object value similarity. Neither subspace tracing nor tracing by object value similarity has been done before.

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References

  1. 1.
    Aggarwal, C.C.: On change diagnosis in evolving data streams. TKDE 17(5), 587–600 (2005)Google Scholar
  2. 2.
    Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: VLDB, pp. 81–92 (2003)Google Scholar
  3. 3.
    Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for projected clustering of high dimensional data streams. In: VLDB, pp. 852–863 (2004)Google Scholar
  4. 4.
    Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. In: SIGMOD, pp. 94–105 (1998)Google Scholar
  5. 5.
    Böttcher, M., Höppner, F., Spiliopoulou, M.: On exploiting the power of time in data mining. SIGKDD Explorations 10(2), 3–11 (2008)CrossRefGoogle Scholar
  6. 6.
    Ester, M., Kriegel, H.P., Jörg, S., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, pp. 226–231 (1996)Google Scholar
  7. 7.
    Gaffney, S., Smyth, P.: Trajectory clustering with mixtures of regression models. In: KDD, pp. 63–72 (1999)Google Scholar
  8. 8.
    Kalnis, P., Mamoulis, N., Bakiras, S.: On discovering moving clusters in spatio-temporal data. In: Anshelevich, E., Egenhofer, M.J., Hwang, J. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 364–381. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Kriegel, H.P., Kröger, P., Zimek, A.: Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. TKDD 3(1), 1–58 (2009)CrossRefGoogle Scholar
  10. 10.
    Li, Y., Han, J., Yang, J.: Clustering moving objects. In: KDD, pp. 617–622 (2004)Google Scholar
  11. 11.
    Müller, E., Günnemann, S., Assent, I., Seidl, T.: Evaluating clustering in subspace projections of high dimensional data. In: VLDB, pp. 1270–1281 (2009)Google Scholar
  12. 12.
    Procopiuc, C.M., Jones, M., Agarwal, P.K., Murali, T.M.: A monte carlo algorithm for fast projective clustering. In: SIGMOD, pp. 418–427 (2002)Google Scholar
  13. 13.
    Rosswog, J., Ghose, K.: Detecting and tracking spatio-temporal clusters with adaptive history filtering. In: ICDM Workshops, pp. 448–457 (2008)Google Scholar
  14. 14.
    Spiliopoulou, M., Ntoutsi, I., Theodoridis, Y., Schult, R.: MONIC - modeling and monitoring cluster transitions. In: KDD, pp. 706–711 (2006)Google Scholar
  15. 15.
    Vlachos, M., Gunopulos, D., Kollios, G.: Discovering similar multidimensional trajectories. In: ICDE, pp. 673–684 (2002)Google Scholar
  16. 16.
    Yiu, M.L., Mamoulis, N.: Frequent-pattern based iterative projected clustering. In: ICDM, pp. 689–692 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Stephan Günnemann
    • 1
  • Hardy Kremer
    • 1
  • Charlotte Laufkötter
    • 2
  • Thomas Seidl
    • 1
  1. 1.Data Management and Data Exploration GroupRWTH Aachen UniversityGermany
  2. 2.Institute of Biogeochemistry and Pollutant DynamicsETH ZürichSwitzerland

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